# NGRC on SoC for online learning of dynamical systems

- Institution: CERN
- Speaker: João Folhadela
- Year: 2026
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3025776)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Predicting the evolution and control of dynamic systems in real time remains a demanding task for conventional methods that depend on accurate models and intensive continuous optimization, which often breaks under uncertainty, noise or delays. Reservoir Computing (RC) provides a lightweight alternative, needing only small training sets and simple inference. We present a low‑cost, low‑power platform that couples next‑generation RC (NGRC) with continuous online learning to forecast long horizons of the Lorenz attractor. The system runs on a Zynq‑7000 SoC: the FPGA accelerates reservoir inference loop, while the ARM CPU manages data acquisition, weight updates and control. With naïve implementation a latency of 148.5 ms on CPU side + 1.5 ms on FPGA is achieved. Results demonstrate that NGRC on modest SoC hardware can deliver energy‑efficient, real‑time predictions of chaotic systems, enabling edge deployment in autonomous vehicles, robotics, satellites and high‑energy physics.

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## Lectures

1. [NGRC on SoC for online learning of dynamical systems](https://invisiblenetwork.ai/learn/cern/3025776/16dd29665708), 20 min (CERN's player)